A – Brief History Data Science is not a new field as the statisticians were doing the job even before the computer invention. Though, the evolution of modern computing technologies empowered statisticians to solve a wide variety of practical problems with heavy number crunching and massive data storage. The terms ‘knowledge discovery’ and ‘data mining’ came widely […]

Data Science is not a new field as the statisticians were doing the job even before the computer invention. Though, the evolution of modern computing technologies empowered statisticians to solve a wide variety of practical problems with heavy number crunching and massive data storage. The terms ‘knowledge discovery’ and ‘data mining’ came widely in use in the late 1980’s after the invention of the database management system and the relational database management system. Later ‘Big data’ term published in the ACM Digital Library in 1997 after the database industry noticed the explosion of business data. In the late 1990’s, the term ‘Data Science’ inspired researchers and professionals and interchangeably replaced the word ‘statistician’.

B- Basic Concept

I- Big Data, Data Science & Machine Learning

Any data with three V’s i.e. Volume, Variety and Velocity is considered as Big Data. Big Data can’t be handled with conventional ways of data analysis and processing. Data science deals with Big Data and brings out meaningful insights. Due to its large scale, Data Science now depends on algorithms that try numerous possibilities to provide the best solution, here comes the Machine Learning.

II- Data Mining & Data Analytics

Machine Learning acts as a tool to identify unknown patterns in the Big Data and the process is called Data Mining, unlike Data analytics where the process starts with a specific hypothesis.

III- Big Data Analytics

The approach to breaking down a task into smaller pieces and assigned to different processors which could be geographically dispersed is called ‘Distributed Computing’. Big data analytics leverages distributed computing technologies to overcome computational challenges.

C- Technologies that Enable Data Science into Reality

– Data Infrastructure: It supports data sharing, processing, and consumption. Distributed computing and cloud computing is most popular these days.

– Data Management: DBMS plays an important role to store structured and unstructured data sets. Since most of business-related data is structured, SQL knowledge is still invaluable.

– Visualization: It is very important to communicate newly acquired insights to the leadership and rest of the organization so data visualization technologies play an equally important role.

D- Data Science Applications

Data Science can be applied where ever ‘Big Data’ is involved. Following are only a few examples:

Fraud detection

Social Media Analytics

Online matchmaking or dating services

Weather forecast

Simulation

Network Security…etc.

E- Must Have Skills for Data Scientist

I- Statistics

Developing a reasonable understanding of statistics is a must for a data scientist as it lays the foundation of data science. At a minimum, a data scientist needs to be proficient with concepts such as probability, correlation, variables, distributions, regression, null hypothesis significance tests, confidence intervals, t-test, ANOVA, and chi-square. At an advanced stage, Data Scientist needs concepts and algorithms such as logistic regression, support vector machines (SVMs) and Bayesian method. Common statistical analysis tools such as Excel, R and SAS are very famous among Data Scientist.

II- Data mining

Classification– Labelling a group of data objects into a specific category.

Prediction– Building a model that produces continuous or ordered values that form a trend.

Clustering– Grouping similar data objects into a class…etc.

Natural Language Processing – NLP refers to different ways for a computer to interact with humans through a natural language. Computer science, Artificial Intelligence (AI), Computer linguistics and Human-computer interaction (HCI) are different areas of NLP. Some of the NLP aspects which are specifically related to Data Science are Tokenization, parsing, sentence, segmentation and named entity recognition. Pythonprogramming language is very famous and a recommended tool for having well-developed NLP tools.

Tokenization and Parsing: Isolate each symbol from a text and conduct a grammatical analysis

Sentence segmentation: Separates one sentence from the other in a text.

Named entity recognition: Identifies which text symbol maps to what types of proper names

Machine Learning(Supervised & Unsupervised)

Visualization– Multiple Software are already available in the market that offers comprehensive visualization tools for data scientist such as Tableau. But it is important to remember that Data Scientist always acts as a middleman between data pile up and decision makers.

F- Roles and Responsibilities

Data Scientist or Engineer

A data scientist can work in any organization who is having data and willing to analyze its performance and future prediction. The role is more of a generalist instead of a specialist. A data scientist works with other data science specialist such as machine learning specialist.

Machine Learning Specialist

It’s a highly creative and independent role where you need the discipline to follow through and meet deadlines. Paying attention to details and quality is critical. Math and IT skills are essential as they form the foundations of the machine learning scientist. Deep knowledge of statistics and probability, ability to develop and validate a mathematical model, translating a model into an algorithm, proficiency in the programming language (Python, C++, Java, R…etc.), understanding of distributed computing are essential skills for a Machine Learning Specialist.

G- Related Certifications

MCSE Business Intelligence Certification

Cloudera Certified Professional or CCP data scientist

Cloudera Certified Developer for Apache Hadoop or CCDH

Cloudera Certified Administrator for Apache Hadoop or CCAH

Cloudera Certified Specialist in Apache HBase or CCSHB

EMC Data Science Associate (EMCDSA)

EMC Data Center Architect or EMCDCA

EMC Cloud Architect or EMCCA

Oracle BI Implementation Specialist …etc.

H- Final Words

Data Scientist must keep refreshing their knowledge to stay up to date. Attending conferences, workshops, peer networking and continuing education are ways to stay updated.

Cloud vendors like Amazon, IBM, and Google …etc. makes it cheaper for companies to use cloud computing facilities instead of private in housed resources, which in turn increases the demand for Data Scientists. Even Data Scientist no longer worries about data infrastructure and management problems due to emerging online services.

The importance of Machine Learning is growing especially deep learning taking advantage of neural networking is getting more traction.

Artificial intelligence is often perceived in the media as robots gone rogue (ie. Westworld or Ex Machina), but there are countless other applications that we don’t realize are already affecting our daily lives – business or personal. These can be in the form of virtual assistants and chatbots, to Siri and Spotify. When it comes […]

Artificial intelligence is often perceived in the media as robots gone rogue (ie. Westworld or Ex Machina), but there are countless other applications that we don’t realize are already affecting our daily lives – business or personal. These can be in the form of virtual assistants and chatbots, to Siri and Spotify.

When it comes to the technology that can influence your internal business processes, AI is changing the game.

Manual data entry is quickly becoming outdated, and in its place solutions are being implemented that reduce time and costs, while increasing accuracy and productivity.

Is it possible to determine who is the champion of invoices when it comes to data capture – traditional OCR or AI?

Round 1: Traditional OCR

What is OCR?

In terms of capturing data from business documents, the long-standing technology has been OCR. Optical character recognition is a fancy way of saying the technology that recognizes text from a document or image. This has been the go-to solution for decades.

Most people thought that this solution solved the manual data entry chore required for capturing data from documents. The output is highly accurate when it comes to documents with low variability.

Although the setup process can be annoying and time-consuming, the reliability of the text recognition is sufficient. OCR is also used in pattern recognition and computerized typesetting, and can now even recognize handwriting.

For OCR to work, rules and templates need to be set up, as shown below for the same field on many different invoices:

Practical Application: Invoices

OCR can be used to accurately capture data from other business documents such as invoices. To perform this task, it is necessary to set up rules and templates for each field.

Another, more practical application where OCR has already been used for decades, and will continue to be a powerhouse is within the postal system. This is because OCR can be designed to mark codes for easier sorting with high-speed sorting machines, and because there is low variability with letters and packages, the process is much easier and the output is accurate.

Difficulties with Traditional OCR:

When it comes to OCR, rules and templates are required in order for the technology to actually capture the necessary data. This means a long and expensive set up process because each individual alteration requires a new rule.

There are also streams of errors that can arise such as false positives from having zero flexibility in regards to document variability. OCR technology cannot be totally automated – there will always need to be more rules set up. For instance, when it comes to invoices, every field needs an individual rule.

Same rules, different invoices:

Round 2: Artificial Intelligence

What is Artificial Intelligence:

According to the Merriam-Webster dictionary, Artificial Intelligence is “a branch of computer science dealing with the simulation of intelligent behavior in computers” with “the capability of a machine to imitate intelligent human behavior”. AI can be used in almost every aspect of our daily lives, from chat bots, to Siri, Netflix, and even the cars we drive (Tesla).

To one day achieve artificial intelligence, it is necessary to utilize artificial neural networks.

What are Neural Networks:

In the simplest terms, a neural network is “a series of algorithms trying to recognize the underlying relationships in sets of data that mimic the way the human mind operates”.

The first proposal of neural networks was in 1944 by Warren McCulloch and Walter Pitts, University professors who were said to be the founding members of the first cognitive science department. Later, in 1954, Belmont Farley and Wesley Clark, from the Massachusetts Institute of Technology, were successful in creating the first artificial neural network. They trained their network to recognize simple patterns which paved the way to neural networks as we know them.

Today, neural networks can be used for financial operations, enterprise planning, trading, business analytics and much more.

Difficulties with AI:

According to Forbes, of companies interviewed, 61% see developing AI as an urgent issue, while only 50% have implemented some kind of AI. At the same time, 83% of respondents believe that AI is a strategic priority for businesses today.

This shows that there seems to be a lack of leadership support for AI initiatives along with a continued cultural resistance to AI technologies. There are still barriers to adoption, especially since AI is still a relatively new phenomenon. It seems that there will be some time before AI is fully accepted and implemented into more businesses.

Practical Application: Invoices

A practical example, where AI can be used to train neural networks is with invoices.

Invoices have a very high variability, and over 18 billion invoices are issued each year in America and Europe alone. To extract data from all of these invoices, it would either require endless manual data entry tasks, or an insane amount of time spent setting up rules and templates for each field, varying for each vendor (as with OCR).

AI can work for other business documents as well, and does not require the same set up of rules or templates, working from day 1. See how AI can do the work on your own invoice.

There is no correct answer to this question. In the end, it will depend on what work needs to be done and what the end goal is. OCR works for certain industries, but is slow and time-consuming for others.

In case of invoices, it comes down to what problem are you solving. AI has a specific advantage compared to traditional OCR in terms of not requiring templates and rules, so the best question to ask is whether this is a barrier for extracting data from invoices in your case.

There are some simple cases where traditional OCR may make more sense – specifically, when all invoices come in the same format or in a few fixed formats, because setting up the rules is pretty straightforward. Another one is extracting highly specific information that is always encoded on an invoice in the same format (say, licence plates or power consumption) – training the AI may be challenging as it does not benefit from the data gathered across all users of the solutions.

But if you are just looking at processing the regular mix of invoices a business receives, the AI is highly accurate and adapts to all kinds of layouts – an AI solution is better and it’s not even a close contest. In practice, developers can then just go ahead and extract data from invoices using AI in only a few lines of code, like in this example. And best of all, as the technology will continue to advance in the next few years, the accuracy and data coverage are only going to get better.

We are living in a 100% data based world. The world’s technological capacity to store information has been doubling every 40 months. According to the World Bank (2012), everyday 2.5×1018 Byte of data is created. This raw data which is created every single day is referred to as Big Data. According to Mckinsey Global Institute, […]

We are living in a 100% data based world. The world’s technological capacity to store information has been doubling every 40 months. According to the World Bank (2012), everyday 2.5×1018 Byte of data is created. This raw data which is created every single day is referred to as Big Data. According to Mckinsey Global Institute, Big Data can be defined as “datasets whose size is beyond the ability of typical database software tools to capture, store, manage and analyze.” (MGI, 2015). The necessity to understand and explore the raw Big data gave naissance to the field of Data science.

Data science can be defined as scientific approach used to derive knowledge and insight from the big and raw data to provide support for decision making. ( Dhar, 2013, P.64) Henceforth, Data science is the application of various technological tools such as algorithms and artificial intelligence to make derive meaning out the huge amount of data which cannot be explored or analyzed by mere use of human intellect or basic technological software. The present article will investigate upon the data science and its necessity in the HR field, will shed light on some trends of data science, and will provide way through which data science can be applied in the HR field.

Data Science as A Necessity For the HR field

With the advancement of technologies and networks used by the organizations, the input of data and information has been of a huge load resulting in a necessity of enhancing its management. The chaotic state of raw data has been of a big struggle for the HR practitioners. The extraction and organization of data for decision -making or recruitment has been of a severe difficulty.

For example, Resumes and Cover letters enclose large amount of subjective and heterogeneous information, processing it by a mere human would certainly lead him or her to ambivalence and confusion. Data science has been really essential in different fields of management as it has to potential to assist in the prediction of employee behavior, supports the analysis of the workforce and Talent, provides evidence based solutions for HR planning and overall business strategy. Nowadays, all decisions have become rather data driven than past experience.

According to Dr. Arup Barman, an Associate professor of Economics at the University of Assam, in all fields or phases of HR, data science has become the must to get higher, better quality, higher accuracy, and cost effective outcome. According to a report elaborated by Villanova University, Data science is a necessity to enhance the overall HR processes. It increases the quality of new hires as it allows the recruiters to be more analytical and to base their strategic thinking on tangible evidences derived and inferred from the big data. It improves the training and employee performance as data science provides accurate measurement of the effectiveness of a particular training.

Also, big data can support in understanding organizational phenomena such as employee engagement and retention. Data science gives the HR practitioner the ability to analyze real time information, get insight upon what is really happening within the organizational framework, and produce a robust manner to track employee performance and address matters to increase the latter or the engagement. Henceforth, Data science is a must in the turbulent and chaotic digital world of data. The overall HR functions would be based on scientific and rea-time evidence to support the quest of thrive and success.

Data science trends

Data science has emerged to make the work of the HR practitioner easier and safer. It has enhanced the overall processes in terms of quality and safety of the outcome. The following section will outline some of the basic trends data science incorporates to be a valid and necessary approach in almost every field.

• Algorithms:

Data science relies on both algorithms and machine learning to perform its tasks. Algorithms are very essential in the data science realm. Algorithms are defined as “well defined computational procedure that takes some value or set of values as input to produce a value or set of values as output. An algorithm is thus a sequence of computational stepts that transforms input into output“( Lisi, 2015, P.23). Henceforth, the algorithm is doomed to be the way through which a data scientists design a particular program to perform a specific duty. It is the language assimilated by a given program. Thus, the algorithm is the tool which transforms the raw data into a meaningful one.

• Predictive analytics:

Data science has been used as a way to predict a specific outcome. The incorporation of predictive analytics to data science in the human resources field can lead into various successful outcomes. It can assist in the forecasting of trends in the Human resources industry. For instance, the incorporation of predictive analytics can use be used to predict employee behavior at the workplace; more precisely, the application of predictive analytics can lead the recruiter to forecast the creativity of a particular candidate at the workplace based on the personality dimensions of the individuals.

• Artificial intelligence and machine learning:

Data science has revolutionized the way data and information is perceived. The most prominent trend among others within the data science field is the artificial intelligence. Don’t worry, machine are not taking over your job. The aim behind developing an artificial intelligence is the simulation of human brain with further computation ability (Russel& Norvig, 1995, P5). Researchers have striven to come up with a system that think and act rationally just like humans. Artificial intelligence is clustered into two types, the Narrow AI and the Strong AI ( Searle, 1980, P.418). An example for the first would be the google translator or your chess game, while for the second; an example would be SIRI, your IPhone assistant which is capable of doing several various tasks. In addition, since the artificial intelligence is design to mimic, imitate, or act like human brain does, it is capable of learning. The algorithms incorporated in the design of an artificial intelligence program are design to allow the machine to learn from its mistakes and experience.

Data science uses in the HR field

The major impacts and benefits data science brings to the business world are really effective in enhancing and polishing all kinds of organizational operations. The human resources, being the heart life of the organizational functions, have received intense focus in terms of development and enhancement as asserted by the following blog. The field of data science promises several developmental opportunities for the human resources. Data science facilitates and ensures the selection of the most appropriate employees for the organization; the implementation of data science trends described above will support the selection of the best-fit candidate for the organization.

Computers overcome the human weakness of biases; henceforth, it enhances the fairness of the selection process. It also facilitates the analysis of thousands resume in a really short amount of time. Accuracy of selection is enhanced through the incorporation of algorithms and machine learning techniques. Henceforth, talent acquisition can become more cost effective. The implementation of data science in the field of HR can produce a better insight about the current and future employees. Data science brings about a more robust and evidence based way of decision making. Managers will rely more on tangible evidences rather than experience and gut feelings.

Tracking employee engagement, assessing organizational needs, or appraising the employee performance will come to be enhanced and be highly accurate with the incorporation of data science trends in the HR field. For instance, the implementation of predictive analytics can facilitate organizational processes by providing the opportunity to forecast employee behavior, foreseen through turnover, and successfully predict employee performance. The HR field, being sensitive to change, data science can help in designing a robust way to cope with the continuous environmental change at both internal and external level. Henceforth, data science provides a robust and resilient support for the strategic design within the organization. It emphasizes on the importance of deriving critical relationship from raw data which cannot be derived by a mere human.

Data science has been vital to survival in the turbulent yet chaotic business world organizations strive to gain the competitive advantage within. Data science brings about a robust and evidence based way to support decision making, enhance strategic thinking, and provides a better insight and understanding of both internal and external environment. Data sciences as well as its trends such as the algorithms, machine learning, artificial intelligence, and predictive analytics have become more of necessity in nowadays business world. The human resources field, being sensitive to change and decisive to the organizational success have brought about the necessity of incorporating data science to support its overall operations. The present article have discussed the necessity of data science for the HR field, provided major trends in the field, and highlighted several uses of the data science in the HR field.

Big Data is the buzz. All companies large and small are investing in the technology today. Although, not all businesses need to invest in Big Data just yet. A Big Data strategy ultimately relies on your specific business pattern, your operation, and your business’ goals and objectives. It is not essential that what works for […]

Big Data is the buzz. All companies large and small are investing in the technology today. Although, not all businesses need to invest in Big Data just yet. A Big Data strategy ultimately relies on your specific business pattern, your operation, and your business’ goals and objectives. It is not essential that what works for them would work for you.

Therefore, if you are continuously bothered by the question of whether or not it would be fruitful for you to invest in Big Data, all you need is a strategy that is based on facts, instead of whims. Here are a few factors you can consider for deciding if this is the right time for you to take the plunge into the Big Data markets-

Analyze your industry – A great way to begin to analyze the possibilities that Big Data can provide for your business is to analyze the industry of your business. From an overall view of your industry, you can gain specific insights about the companies that are already investing in Big Data, if there is a lot of buzz around Big Data if the buzz is real or only hyped, etc. Then, review what you have assimilated. See if Big Data is largely and substantially affecting businesses in your industry. If it is, maybe it is time you should invest, too.

Customer engagement – If customer engagement and customer experience is an integral part of your business, a Big Data strategy can do wonders for you. Data analysis is helping enterprises realize what their customers want, and is also helping them predict trends. This will give you an edge over your competitors in your market. An effective Big Data strategy can help you establish a monopoly in your industry.

Market trends – If your business can benefit largely from predicting the market trends, and if you think it is the right time for you to make an investment in that direction, Big Data is a gem for you. For forging new partnerships or satisfying customers, predicting the market needs well in time is the key. Add to this your efforts in activities like customer support, providing guarantees, etc., and you have a Big Data strategy that works!

Data volume – Another factor to consider is if you have the voluminous data being generated from multiple sources regularly because, without that, Big Data cannot begin. You need to have a significant amount of data that is valuable, to embark on your Big Data journey. With this, you also need to perform an analysis if your company can afford to make investments in storing huge volumes of data.

Existing resources – Are you ready to hire human resources for working with your data? Or, do you already have data engineers on your team? Determine all these investments beforehand, so that you are not in for a big surprise. Big Data analysis requires the help of data engineers and data professionals to bring fruitful results. This is a truth that cannot be shaken.

If all these factors seem to work fine for you, Big data consulting services is a winning strategy that you can invest in. Remember, it is not essential to do what all are doing, but to do what you think your business demands. Big Data is a technology that is relevant in all industries, so gear up, because it is inevitable.

]]>https://www.datascience.us/should-i-invest-in-big-data-look-for-these-signs/feed/0What is the Holt-Winters Forecasting Algorithm and How Can it be Used for Enterprise Analysis?https://www.datascience.us/what-is-the-holt-winters-forecasting-algorithm-and-how-can-it-be-used-for-enterprise-analysis/
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This article provides a brief explanation of the Holt-Winters Forecasting model and its application in the business environment. What is the Holt-Winters Forecasting Algorithm? The Holt-Winters algorithm is used for forecasting and It is a time-series forecasting method. Time series forecasting methods are used to extract and analyze data and statistics and characterize results to […]

This article provides a brief explanation of the Holt-Winters Forecasting model and its application in the business environment.

What is the Holt-Winters Forecasting Algorithm?

The Holt-Winters algorithm is used for forecasting and It is a time-series forecasting method. Time series forecasting methods are used to extract and analyze data and statistics and characterize results to more accurately predict the future based on historical data.

For more information about data trend and pattern analysis techniques, read our article entitled, ‘What Are Data Trends and Patterns, and How Do They Impact Business Decisions?’

The Holt-Winters forecasting algorithm allows users to smooth a time series and use that data to forecast areas of interest. Exponential smoothing assigns exponentially decreasing weights and values against historical data to decrease the value of the weight for the older data. In other words, more recent historical data is assigned more weight in forecasting than the older results.

There are three types of exponential smoothing methods used in Holt-Winters:

Single Exponential Smoothing – suitable for forecasting data with no trend or seasonal pattern, where the level of the data may change over time.

Time-Series forecasting methods and, in particular, the Holt-Winters forecasting algorithm can be helpful in providing forecasts for planning purposes by using historical data in a meaningful way. Because the results are smoothed, and the user can select the best option for the TYPE of data to be analyzed, the enterprise can avoid assigning too much weight or importance to older data that may no longer be as valid because of changing buying behaviors, market competition or other factors.

Let’s look at a few use cases that represent ideal examples of the various Holt-Winters exponential smoothing methods:

1) Single Exponential Smoothing Use Case

Business Problem: Forecasting number of viewers by day for a particular game show for next two months.

Input Data: Last six months daily viewer count data.

Data Pattern: Data taken as an input exhibit no trend /seasonality.

Business Benefit: Helps in planning for repeat telecast and for more advertisement (fund raising) if the projected count of viewers is high. Improvement planning can be done for the game show to increase/maintain the level of popularity.

2) Double Exponential Smoothing Use Case

Business Problem: Insurance claim manager wants to forecast policy sales for next month based on past 12 months data.

Business Benefit: If projected claims are lower than expected then proper marketing strategy can be devised to improve sales. Competition policy can be analyzed in terms of what all perks and benefits they provide to customers and existing policy can be modified to increase the market share.

3) Triple Exponential Smoothing Use Case

Business Problem: A power generator company wants to predict the electricity demand for next two months based on past 2 years’ daily power consumption data.

Data Pattern: Input data exhibits trend and seasonality.

Business Benefit: A power generator company can make use of these forecasts for the control and scheduling of power systems or power purchase agreements. It helps in balancing supply and demand.

When users select the appropriate forecasting algorithm for the data they wish to analyze, they can produce and share reports and data that will provide clear direction and decision support. In order to achieve the right results, it is imperative that a user select the right forecasting algorithm, based on the pattern and underlying data. Tools such as Smarten Plug n’ Play predictive analysis provide assisted predictive modeling capabilities. These augmented analytics tools use machine learning to auto-detect and recommend the best algorithm so users do not have to guess at the right selection. Smart Visualization ensures that data and its interpretation are clearly depicted in simple, natural language.

To provide flexible business intelligence and forecasting tools and ensure data democratization among business users, as well as accurate planning methods, an enterprise must select tools that are easy-to-use and easy to implement. The solution must include a full suite of advanced analytics tools to empower business users and create Citizen Data Scientists whose contribution to the organization will be a real asset and a true contributor to business results.

In 2018 Fast Company declared the Data Scientist the best job for the third year in a row, which I wholeheartedly agree with (besides the Director of Fun at the York National Railway Museum), however the role of data scientist, as we know it, will soon have the same fate as the bowling pinsetters, chariot […]

In 2018 Fast Company declared the Data Scientist the best job for the third year in a row, which I wholeheartedly agree with (besides the Director of Fun at the York National Railway Museum), however the role of data scientist, as we know it, will soon have the same fate as the bowling pinsetters, chariot racers, and human alarm clocks.

In 2000-2010 data science was dominated by masters of herculean subjects, with PhDs in linear algebra and statistics, combined with expertise in the uncelebrated (at the time) field of coding. Data science truly had an emphasis on the science of manipulating data, focusing on how to mathematically validate significance and trends. This was a great first step in helping society gain insights from the massive influx of big data, however it now has its drawbacks.

Tipping the balance too far towards degrees of freedom and vectors is great in the ivory towers of academics, but when it comes to practical and timely results for businesses, is not ideal. I recently heard a story about a team of PhD data scientists at a Fortune 500 company having trouble improving their built from scratch multi-layered neural network model’s accuracy. They spent hours meticulously tuning cryptic hyper-parameters and adding layers to their model with no success. The data then ended up falling into the hands of an employee fresh out of his undergraduate degree. After quickly looking at the data, his first step was to create a simple regression model and remove all zero values, immediately skyrocketing accuracy, and creating a cluster of self-conscious PhDs. Despite his lack of experience with scalar multiplication or multi-threading programming, his domain and practical knowledge made all of the difference.

With the increasing power of user-friendly tools and GUIs, and a data science course seemingly available on every website, being able to perform data science will eventually be like being competent in Excel. Just knowing the ins and outs of data science as a skill will not be enough. The tools will be powerful enough to handle the data “sciencey” aspects, and the fundamental concepts will be taught throughout school, evolving data science into a skill integral to every job role, not a title. There will be no more data scientist roles, just roles that use data science.

For now, before the data scientist role goes into retirement, these forces of user-friendly tools and democratization of knowledge is increasing the potential of beginner data scientists to get powerful results with the right training. Beginner data scientists are spearheading advanced AI across Fortune 500 companies developing deep learning computer vision and natural language processing models for predictive maintenance of assets, facial recognition, and generating valuable insights from social media and news. Data science managers should be raising their expectations of what their teams can achieve, and be willing to invest in training their teams to get them confident with advanced techniques.

Ultimately, although the role of data scientist may be in its golden years, it still currently has amazing opportunities to create transformational changes across businesses, and should be leading the odds to fourpeat Fast Company’s best job award in 2019.

Get a free case study on how a Fortune 500 company is saving money using computer vision for predictive maintenance at textvox.ai

So, it’s 2018 and the word is spread about Data boom. There are Tech Giants like Facebook, Amazon, and Google constantly working in the field of Machine learning and Data science. We all know that Machine learning, Data Sciences, and Data analytics is the future. There companies like Cambridge Analytica, and other data analysis companies […]

So, it’s 2018 and the word is spread about Data boom. There are Tech Giants like Facebook, Amazon, and Google constantly working in the field of Machine learning and Data science.

We all know that Machine learning, Data Sciences, and Data analytics is the future. There companies like Cambridge Analytica, and other data analysis companies who not only help businesses predict the future growth and generate revenue but also find the application in other fields like survey, product launch, elections and what not. Stores like Target and Amazon constantly keep a track of user data in forms of their transactions, which in turn helps them to improve their user experience and deploy custom recommendations for you on your login page.

Well, we have discussed the trend, so let’s get a little deeper and explore their differences. While Machine Learning, Data Sciences, and Data analytics can’t be exclusively separated, as they are pretty much originating from the same concepts just different applications. They all go hand in hand with each other, and you’ll easily find an overlap between them too.

Data science

So, what is this data science?

Data science is a concept used to tackle and monitor huge amounts of data or big data. Data science includes process like data cleansing, preparation, and analysis. A data scientist would collect data from multiple sources like surveys, physical data plotting. He would then make the data pass through the vigorous algorithms to extract the critical information from the data and make a data set. This dataset could be further be fed to analyzing algorithms, to make more meaning out of it. Which is what basically Data analytics is pretty much for.

What skills are required to make Data scientist?

Some key skills that you’d need :
Deep knowledge of Python, Scala, SAS.
Knowledge of databases like SQL.
Good knowledge in the field of Mathematics and statistics.
Understanding of analytical functions.
Knowledge and experience in machine learning.

Now, you might be wondering ” What is data analytics then?”

Talking in terms of a layman, if Data science is a house that consists of all the tools and resources. Data analytics would be a specific room. It is more specific in terms of functionality and application. Instead of just looking for connections like we do in Data science, a data analyst have a specific aim and goal. Data analytics is often used by the companies to search for trends in their growth. It often moves data insights to impact by connecting the dots between trends and pattern while Data science is more about just insights. You could say that this field is more focused on businesses and organizations and their growth. You would need skills like, Python, Rlab, Statistics, Economics, and Mathematics to become a Data analyst.

Data analytics further bifurcates into branches like Data mining, which involves sorting through datasets and identify relationships.
Predictive analytics. This generally includes predicting customer behavior and product impact. Helps during the market research. Makes the data collected from surveys more usable and accurate in predictions. This finds application in a number of places. From weather report generation to predicting a students behavior in schools to predict the outbreak of disease.

To conclude, one can obviously not draw a definite and clear line between Data analytics and Data science, but a Data scientist would have pretty much the same concepts and skills as an experienced data scientist. The difference between both of them would be the area of applications.

Remember how you learned to ride a bicycle? A machine could learn that with the help of algorithms and datasets. Datasets of values basically.

Machine Learning, basically comprises of set of algorithms that could make software and program learn from it’s past experiences and thus make it more accurate in predicting outcomes. This doesn’t need to be explicitly programmed, as the algorithm improves and adapts itself overtime.

There are overlaps and differences between Machine Learning and Data science.

Machine learning and data analytics are a part of data science. Because the machine learning algorithm obviously depends on some data to learn. Data science is a broader term and would not only focus on implementing algorithms and statistics but it includes the entire data processing methodology.

Thus, data science is a broader term that could incorporate multiple concepts like data analytics, machine learning, predictive analytics and Business analytics.

However, Machine learning finds applications in the fields where Data science can’t standalone like Face ID, fingerprint scanner, voice recognition, robotics. Recently, Google taught it’s robot to walk, the algorithms only had constraints and physical parameters of the contour on the robot was supposed to walk. There was no other dataset included, the Machine walked through many different cases and made its dataset of the values it could refer to. Hence, after a few trials and errors, It learned to walk in a few days. This is the best example of Machine learning, that machine actually learns and changes its behavior.

]]>https://www.datascience.us/difference-between-data-science-data-analytics-and-machine-learning/feed/0What Are Data Trends and Patterns, and How Do They Impact Business Decisions?https://www.datascience.us/what-are-data-trends-and-patterns-and-how-do-they-impact-business-decisions/
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The business can use this information for forecasting and planning, and to test theories and strategies. Let’s look at the various methods of trend and pattern analysis in more detail so we can better understand the various techniques. Linear Trend A linear pattern is a continuous decrease or increase in numbers over time. On a […]

The business can use this information for forecasting and planning, and to test theories and strategies. Let’s look at the various methods of trend and pattern analysis in more detail so we can better understand the various techniques.

Linear Trend

A linear pattern is a continuous decrease or increase in numbers over time. On a graph, this data appears as a straight line angled diagonally up or down (the angle may be steep or shallow). So the trend either can be upward or downward.

Exponential Trend

This technique produces non linear curved lines where the data rises or falls, not at a steady rate, but at a higher rate. Instead of a straight line pointing diagonally up, the graph will show a curved line where the last point in later years is higher than the first year, if the trend is upward.

Damped Trend

In this analysis, the line is curved line to show data values rising or falling initially, and then showing a point where the trend (increase or decrease) stops rising or falling.

Seasonality

One can identify a seasonality pattern when fluctuations repeat over fixed periods of time and are therefore predictable and where those patterns do not extend beyond a one year period. Seasonality may be caused by factors like weather, vacation, and holidays. It usually consists of periodic, repetitive, and generally regular and predictable patterns. Seasonality can repeat on a weekly, monthly or quarterly basis.

Irregular/Random Patterns

This type of analysis reveals fluctuations in a time series. These fluctuations are short in duration, erratic in nature and follow no regularity in the occurrence pattern. In prediction, the objective is to “model” all the components to some trend patterns to the point that the only component that remains unexplained is the random component.

Stationary/Stationarity

A stationary time series is one with statistical properties such as mean, where variances are all constant over time. A stationary series varies around a constant mean level, neither decreasing nor increasing systematically over time, with constant variance.

Cyclical Patterns

Cyclical patterns occur when fluctuations do not repeat over fixed periods of time and are therefore unpredictable and extend beyond a year.

In this article, we have reviewed and explained the types of trend and pattern analysis. Every dataset is unique, and the identification of trends and patterns in the underlying the data is important. If a business wishes to produce clear, accurate results, it must choose the algorithm and technique that is the most appropriate for a particular type of data and analysis. For example, the decision to the ARIMA or Holt-Winter time series forecasting method for a particular dataset will depend on the trends and patterns within that dataset.

A basic understanding of the types and uses of trend and pattern analysis is crucial, if an enterprise wishes to take full advantage of these analytical techniques and produce reports and findings that will help the business to achieve its goals and to compete in its market of choice.